Explore the CAZome of Pectobacteriaceae genomes

This notebook explores the size and composition of 717 Genbank Pectobacteriaceae CAZomes.

GitHub

Information of the complete method for this analysis, including augmenting the dataset, a README-walkthrough, and the output figure files, can be found in the GitHub repository.

Table of Contents

1. Imports
   • Load packages
   • Load in data
2. CAZome size
   • Compare the number of CAZymes
   • Compare the proportion of the proteome represented by the CAZomes
3. CAZy classes
   • The number of CAZymes per CAZy class
   • Mean (+/- SD) number of CAZymes per CAZy class per genus
4. CAZy families
   • Calculate CAZy family frequencies per genome
   • Plot a clustermap of CAZy family frequencies
5. Core CAZome
   • Identify families that are present in all genomes
   • Calculate the frequency of families in the core CAZome
   • Pectobacterium core CAZome
   • Dickeya
6. Always co-occurring families
   • Identify CAZy families that are always present in the genome together
   • Explore across all Pectobacterium and Dickeya genomes, and per genus
   • Build an upset plot of co-occurring CAZy families
   • Compile a matrix with the indcidence data for each group of co-occurring CAZy families
7. Principal Component Analysis (PCA)
   • Explore the association between the host range, global distribution and composition of the CAZome
   • Explore across all Pectobacterium and Dickeya genomes, and per genus

0. Imports

Packages

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statistics
import re
import time

from copy import copy
from matplotlib.patches import Patch
from pathlib import Path
import upsetplot
import adjustText
import upsetplot

from Bio import SeqIO
from saintBioutils.utilities.file_io.get_paths import get_file_paths
from saintBioutils.utilities.file_io import make_output_directory
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from tqdm.notebook import tqdm

%matplotlib inline

# loading and parsing data
from cazomevolve.cazome.explore.parse_data import (
    load_fgp_data,
    load_tax_data,
    add_tax_data_from_tax_df,
    add_tax_column_from_row_index,
)

# functions for exploring the sizes of CAZomes
from cazomevolve.cazome.explore.cazome_sizes import (
    calc_proteome_representation,
    count_items_in_cazome,
    get_proteome_sizes,
    count_cazyme_fam_ratio,
)

# explore the frequency of CAZymes per CAZy class
from cazomevolve.cazome.explore.cazy_classes import calculate_class_sizes

# explore the frequencies of CAZy families and identify the co-cazome
from cazomevolve.cazome.explore.cazy_families import (
    build_fam_freq_df,
    build_row_colours,
    build_family_clustermap,
    identify_core_cazome,
    plot_fam_boxplot,
    build_fam_mean_freq_df,
    get_group_specific_fams,
    build_family_clustermap_multi_legend,
)

# functions to identify and explore CAZy families that are always present together
from cazomevolve.cazome.explore.cooccurring_families import (
    identify_cooccurring_fams_corrM,
    calc_cooccuring_fam_freqs,
    identify_cooccurring_fam_pairs,
    add_to_upsetplot_membership,
    build_upsetplot,
    get_upsetplot_grps,
    add_upsetplot_grp_freqs,
    build_upsetplot_matrix,
)

# functions to perform PCA
from cazomevolve.cazome.explore.pca import (
    perform_pca,
    plot_explained_variance,
    plot_scree,
    plot_pca,
    plot_loadings,
)

Output directory

Make the parent output directory. Make subdirectories as and when needed throughout the notebook.

Use the function make_output_directory from saintBioutils. One positional argument is required: the path to the target output directory to be build - this must be a Path object.

Always set force and nodelete to True - this ensures the output directory is created, and if it exists content in the output directory is not deleted.

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results'), force=True, nodelete=True)

Output directory ../results exists, nodelete is True. Adding output to output directory.

Data

CAZy family annotations: The GFP file

Load tab delimited list of cazy families, genomes and protein accessions, by providing the path to the 'gfp file' to load_gfp_data().

Each unique protein-family pair is represented on a separate line. Owing to a protein potentially containing multiple CAZyme domains and thus can be annotated with multiple CAZy families, a single protein can be present on multiple rows in the gfp_df.

fgp_file = "../data/cazomes/pecto_fam_genomes_proteins"
fgp_df = load_fgp_data(fgp_file)
fgp_df.head(3)

	Family	Genome	Protein
0	CBM50	GCA_003382565.3	UEM40323.1
1	GT35	GCA_003382565.3	UEM39157.1
2	GH5	GCA_003382565.3	UEM41238.1

print(f"Total CAZymes: {len(set(fgp_df['Protein']))}")

Total CAZymes: 78132

Taxonomy data:

Load in CSV of tax data from generated by cazevolve_add_taxs, by providing a path to the file to load_tax_data(), and specify which tax ranks (kingdom, phylum, etc.) are included in the CSV file.

tax_csv_path = "../data/cazomes/fg_genome_taxs.csv"
tax_df = load_tax_data(tax_csv_path, genus=True, species=True)
tax_df.head(3)

	Genome	Genus	Species
0	GCA_922021645.1	Pectobacterium	versatile
1	GCA_004296685.1	Pectobacterium	versatile
2	GCA_018094705.1	Pectobacterium	versatile

Compile all data into a single dataframe:

Build dataframe of:

• CAZy family annotations
• Genomic accession
• Taxonomic information - splitting each taxonomy rank (i.e. ranks) into a separate column. E.g.:
  • Genus
  • Species

fgp_df = add_tax_data_from_tax_df(
    fgp_df,
    tax_df,
    genus=True,
    species=True,
)
fgp_df.head(3)

Collecting Genus data: 100%|██████████| 83143/83143 [01:00<00:00, 1383.74it/s]
Collecting Species data: 100%|██████████| 83143/83143 [01:02<00:00, 1329.79it/s]

	Family	Genome	Protein	Genus	Species
0	CBM50	GCA_003382565.3	UEM40323.1	Pectobacterium	aquaticum
1	GT35	GCA_003382565.3	UEM39157.1	Pectobacterium	aquaticum
2	GH5	GCA_003382565.3	UEM41238.1	Pectobacterium	aquaticum

print(f"Total CAZymes: {len(set(fgp_df['Protein']))}")

Total CAZymes: 78132

1. CAZome size

Calculate the number of CAZymes per genome (defined as the number of unique protein accessions per genome).

In total, calculate:

• The number of CAZymes per genome
• The mean number of CAZymes per genome per genus
• The proportion of the proteome represented by the CAZome
• The mean proportion of the proteome represented by the CAZome

Use the count_items_in_cazome() function to retrieve the number of CAZymes and the number of CAZy families per genome, and the mean counts per genus.

# check all genomes are represented in the fgp_df
f"Examining {len(set(fgp_df['Genome']))} genomes"

'Examining 717 genomes'

print(f"Examining {len(set(fgp_df['Genus']))} genera:")
for genus in set(fgp_df['Genus']):
    print(f'- {genus}')

Examining 8 genera:
- Samsonia
- Dickeya
- Acerihabitans
- Musicola
- Pectobacterium
- Brenneria
- Affinibrenneria
- Lonsdalea

# Calculate CAZymes per genome
cazome_sizes_dict, cazome_sizes_df = count_items_in_cazome(fgp_df, 'Protein', 'Genus', round_by=2)
cazome_sizes_df

Gathering CAZy families per genome: 100%|██████████| 83143/83143 [00:09<00:00, 8434.63it/s]
Calculating num of Protein per genome and per Genus: 100%|██████████| 8/8 [00:00<00:00, 2474.33it/s]

	Genus	MeanProteins	SdProteins	NumOfGenomes
0	Pectobacterium	112.65	8.02	432
1	Dickeya	111.16	6.60	206
2	Musicola	92.25	2.28	4
3	Brenneria	87.79	7.46	33
4	Lonsdalea	77.15	4.70	39
5	Acerihabitans	106.00	0.00	1
6	Affinibrenneria	108.00	0.00	1
7	Samsonia	81.00	0.00	1

# calculate mean across pectobacteriaceae
pectobact_cazome_sizes = []
for genus in cazome_sizes_dict:
    for genome in cazome_sizes_dict[genus]:
        pectobact_cazome_sizes.append(cazome_sizes_dict[genus][genome]['numOfProteins'])

pd.concat(
    [
        cazome_sizes_df, 
        pd.DataFrame(
            [[
                'Pectobacteriaceae',
                np.mean(pectobact_cazome_sizes),
                np.std(pectobact_cazome_sizes),
                len(set(fgp_df['Genome'])),
            ]], 
            columns=cazome_sizes_df.columns
        ),
    ],
    axis=0,
)

	Genus	MeanProteins	SdProteins	NumOfGenomes
0	Pectobacterium	112.650000	8.020000	432
1	Dickeya	111.160000	6.600000	206
2	Musicola	92.250000	2.280000	4
3	Brenneria	87.790000	7.460000	33
4	Lonsdalea	77.150000	4.700000	39
5	Acerihabitans	106.000000	0.000000	1
6	Affinibrenneria	108.000000	0.000000	1
7	Samsonia	81.000000	0.000000	1
0	Pectobacteriaceae	108.970711	11.958225	717

# Calculate CAZy families per genome
cazome_fam_dict, cazome_fams_df = count_items_in_cazome(fgp_df, 'Family', 'Genus', round_by=2)
cazome_fams_df

Gathering CAZy families per genome: 100%|██████████| 83143/83143 [00:09<00:00, 8389.96it/s]
Calculating num of Family per genome and per Genus: 100%|██████████| 8/8 [00:00<00:00, 2855.94it/s]

	Genus	MeanFamilys	SdFamilys	NumOfGenomes
0	Pectobacterium	62.08	3.46	432
1	Dickeya	59.05	3.04	206
2	Musicola	50.25	0.43	4
3	Brenneria	53.67	3.71	33
4	Lonsdalea	42.62	2.14	39
5	Acerihabitans	48.00	0.00	1
6	Affinibrenneria	48.00	0.00	1
7	Samsonia	49.00	0.00	1

# calculate mean across pectobacteriaceae
pectobact_fam_nums = []
for genus in cazome_fam_dict:
    for genome in cazome_fam_dict[genus]:
        pectobact_fam_nums.append(cazome_fam_dict[genus][genome]['numOfFamilys'])

pd.concat(
    [
        cazome_fams_df, 
        pd.DataFrame(
            [[
                'Pectobacteriaceae',
                np.mean(pectobact_fam_nums),
                np.std(pectobact_fam_nums),
                len(set(fgp_df['Genome'])),
            ]], 
            columns=cazome_fams_df.columns
        ),
    ],
    axis=0,
)

	Genus	MeanFamilys	SdFamilys	NumOfGenomes
0	Pectobacterium	62.080000	3.460000	432
1	Dickeya	59.050000	3.040000	206
2	Musicola	50.250000	0.430000	4
3	Brenneria	53.670000	3.710000	33
4	Lonsdalea	42.620000	2.140000	39
5	Acerihabitans	48.000000	0.000000	1
6	Affinibrenneria	48.000000	0.000000	1
7	Samsonia	49.000000	0.000000	1
0	Pectobacteriaceae	59.638773	5.733681	717

Identify the total number of CAZymes

print(f"The total number of CAZymes is {len(set(fgp_df['Protein']))}")

for genus in set(fgp_df['Genus']):
    genus_df = fgp_df[fgp_df['Genus'] == genus]
    print(f"The total number of {genus} CAZymes is {len(set(genus_df['Protein']))}")

The total number of CAZymes is 78132
The total number of Samsonia CAZymes is 81
The total number of Dickeya CAZymes is 22899
The total number of Acerihabitans CAZymes is 106
The total number of Musicola CAZymes is 369
The total number of Pectobacterium CAZymes is 48663
The total number of Brenneria CAZymes is 2897
The total number of Affinibrenneria CAZymes is 108
The total number of Lonsdalea CAZymes is 3009

Look at the ratio of CAZymes to CAZy families.

cazome_ratio_dict, cazome_ratio_df = count_cazyme_fam_ratio(fgp_df, 'Genus', round_by=2)
cazome_ratio_df

Gathering CAZymes and CAZy families per genome: 100%|██████████| 83143/83143 [00:10<00:00, 8065.74it/s]
Calculating CAZyme/CAZy family ratio: 100%|██████████| 8/8 [00:00<00:00, 1878.54it/s]

	Genus	MeanCAZymeToFamRatio	SdCAZymeToFamRatio	NumOfGenomes
0	Pectobacterium	1.81	0.09	432
1	Dickeya	1.88	0.07	206
2	Musicola	1.84	0.04	4
3	Brenneria	1.64	0.07	33
4	Lonsdalea	1.81	0.07	39
5	Acerihabitans	2.21	0.00	1
6	Affinibrenneria	2.25	0.00	1
7	Samsonia	1.65	0.00	1

pecto_ratios = []
for genus in cazome_sizes_dict:
    for genome in cazome_sizes_dict[genus]:
        ratio = (
            cazome_sizes_dict[genus][genome]['numOfProteins'] / cazome_fam_dict[genus][genome]['numOfFamilys']
        )
        pecto_ratios.append(ratio)

pd.concat(
    [
        cazome_ratio_df,
        pd.DataFrame(
            [[
                'Pectobacteriaceae',
                np.mean(pecto_ratios),
                np.std(pecto_ratios),
                len(set(fgp_df['Genome'])),
            ]],
            columns=cazome_ratio_df.columns
        )
    ],
    axis=0,
)

	Genus	MeanCAZymeToFamRatio	SdCAZymeToFamRatio	NumOfGenomes
0	Pectobacterium	1.810000	0.09000	432
1	Dickeya	1.880000	0.07000	206
2	Musicola	1.840000	0.04000	4
3	Brenneria	1.640000	0.07000	33
4	Lonsdalea	1.810000	0.07000	39
5	Acerihabitans	2.210000	0.00000	1
6	Affinibrenneria	2.250000	0.00000	1
7	Samsonia	1.650000	0.00000	1
0	Pectobacteriaceae	1.826642	0.09796	717

Proteome sizes:

# Get the size of the proteome (the number of protein acc) per genome
grp = 'Genus'
proteome_dir = "../data/proteomes"
proteome_dict = get_proteome_sizes(proteome_dir, fgp_df, grp)

Getting proteome sizes: 100%|██████████| 717/717 [00:46<00:00, 15.45it/s]

# get total number of proteins across all proteomes
total_proteins = 0
for genus in proteome_dict:
    for genome in proteome_dict[genus]:
        total_proteins += proteome_dict[genus][genome]['numOfProteins']
print(f"Total number of proteins across all genomes: {total_proteins}")

Total number of proteins across all genomes: 2994018

# Calculate the mean proteome size by genus and the proportion of the proteome represented by the CAZome
proteome_perc_df = calc_proteome_representation(proteome_dict, cazome_sizes_dict, grp, round_by=2)
proteome_perc_df

Getting proteome size: 100%|██████████| 8/8 [00:00<00:00, 8555.44it/s]
Calc proteome perc: 100%|██████████| 8/8 [00:00<00:00, 2680.07it/s]

	Genus	MeanProteomeSize	SdProteomeSize	MeanProteomePerc	SdProteomePerc	NumOfGenomes
0	Pectobacterium	4260.71	216.78	2.64	0.15	432
1	Dickeya	4176.86	155.23	2.66	0.11	206
2	Musicola	3992.00	55.76	2.31	0.05	4
3	Brenneria	4270.24	478.85	2.07	0.15	33
4	Lonsdalea	3142.28	132.55	2.45	0.09	39
5	Acerihabitans	4969.00	0.00	2.13	0.00	1
6	Affinibrenneria	5064.00	0.00	2.13	0.00	1
7	Samsonia	3489.00	0.00	2.32	0.00	1

pectobact_average = ['Pectobacteriaceae']
for col in proteome_perc_df.columns[1:]:
    pectobact_average.append(np.mean(list(proteome_perc_df[col])))
pectobact_average[-1] == 660

df = pd.DataFrame([pectobact_average], columns=proteome_perc_df.columns)
pd.concat([proteome_perc_df, df], ignore_index=True, axis=0).round(2)

	Genus	MeanProteomeSize	SdProteomeSize	MeanProteomePerc	SdProteomePerc	NumOfGenomes
0	Pectobacterium	4260.71	216.78	2.64	0.15	432.00
1	Dickeya	4176.86	155.23	2.66	0.11	206.00
2	Musicola	3992.00	55.76	2.31	0.05	4.00
3	Brenneria	4270.24	478.85	2.07	0.15	33.00
4	Lonsdalea	3142.28	132.55	2.45	0.09	39.00
5	Acerihabitans	4969.00	0.00	2.13	0.00	1.00
6	Affinibrenneria	5064.00	0.00	2.13	0.00	1.00
7	Samsonia	3489.00	0.00	2.32	0.00	1.00
8	Pectobacteriaceae	4170.51	129.90	2.34	0.07	89.62

For easier comparison and presentation, combine the dataframes made above into a single dataframe, with each row representing a different genus.

all_df = pd.concat([proteome_perc_df, cazome_sizes_df, cazome_fams_df, cazome_ratio_df], axis=1, join='inner')
make_output_directory(Path('../results/cazome_size'), force=True, nodelete=True)
all_df.to_csv('../results/cazome_size/cazome_sizes.csv')
all_df

Built output directory: ../results/cazome_size

	Genus	MeanProteomeSize	SdProteomeSize	MeanProteomePerc	SdProteomePerc	NumOfGenomes	Genus	MeanProteins	SdProteins	NumOfGenomes	Genus	MeanFamilys	SdFamilys	NumOfGenomes	Genus	MeanCAZymeToFamRatio	SdCAZymeToFamRatio	NumOfGenomes
0	Pectobacterium	4260.71	216.78	2.64	0.15	432	Pectobacterium	112.65	8.02	432	Pectobacterium	62.08	3.46	432	Pectobacterium	1.81	0.09	432
1	Dickeya	4176.86	155.23	2.66	0.11	206	Dickeya	111.16	6.60	206	Dickeya	59.05	3.04	206	Dickeya	1.88	0.07	206
2	Musicola	3992.00	55.76	2.31	0.05	4	Musicola	92.25	2.28	4	Musicola	50.25	0.43	4	Musicola	1.84	0.04	4
3	Brenneria	4270.24	478.85	2.07	0.15	33	Brenneria	87.79	7.46	33	Brenneria	53.67	3.71	33	Brenneria	1.64	0.07	33
4	Lonsdalea	3142.28	132.55	2.45	0.09	39	Lonsdalea	77.15	4.70	39	Lonsdalea	42.62	2.14	39	Lonsdalea	1.81	0.07	39
5	Acerihabitans	4969.00	0.00	2.13	0.00	1	Acerihabitans	106.00	0.00	1	Acerihabitans	48.00	0.00	1	Acerihabitans	2.21	0.00	1
6	Affinibrenneria	5064.00	0.00	2.13	0.00	1	Affinibrenneria	108.00	0.00	1	Affinibrenneria	48.00	0.00	1	Affinibrenneria	2.25	0.00	1
7	Samsonia	3489.00	0.00	2.32	0.00	1	Samsonia	81.00	0.00	1	Samsonia	49.00	0.00	1	Samsonia	1.65	0.00	1

# calculate means for Pectobacteriaceae
for col in all_df:
    if col == 'Genus' or col == 'NumOfGenomes':
        continue
    print(col, '--', np.mean(list(all_df[col])).round(2))

MeanProteomeSize -- 4170.51
SdProteomeSize -- 129.9
MeanProteomePerc -- 2.34
SdProteomePerc -- 0.07
MeanProteins -- 97.0
SdProteins -- 3.63
MeanFamilys -- 51.58
SdFamilys -- 1.6
MeanCAZymeToFamRatio -- 1.89
SdCAZymeToFamRatio -- 0.04

2. CAZy classes

Calculate the number of CAZymes (identified as the number of unique protein accessions) per CAZy class. Also, calculate the mean size of CAZy classes (i.e. the mean number of unique protein accessions per CAZy class in each genome) per genus.

The results are added to a dataframe, which is written to results/pecto_dic/cazy_class_sizes.csv, and was used to generate a proportiona area plot using RawGraphs.

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/cazy_classes/'), force=True, nodelete=True)

Built output directory: ../results/cazy_classes

class_df, class_size_dict = calculate_class_sizes(fgp_df, 'Genus', round_by=2)

Getting CAZy class sizes: 100%|██████████| 83143/83143 [00:35<00:00, 2314.29it/s]
Calculating CAZy class sizes: 100%|██████████| 6/6 [00:00<00:00, 129.17it/s]

# add values with means across all genera to represent pectobacteriaceae
pectobact_class_means = []

for cazy_class in set(class_df['CAZyClass']):
    df = class_df[class_df['CAZyClass'] == cazy_class]
    new_row = [cazy_class, 'Pectobacteriaceae']
    
    for col in class_df.columns[2:]:
        mean = np.mean(df[col])
        new_row.append(mean)
    
    new_row[-1] = 660
    
    pectobact_class_means.append(new_row)

df = pd.DataFrame(pectobact_class_means, columns = class_df.columns)
all_class_df = pd.concat([class_df, df], axis=0, ignore_index=True)
all_class_df = all_class_df.round(2)
# replace nan with 0
all_class_df = all_class_df.fillna(0)

filtered_class_df = all_class_df[all_class_df['Genus'] != 'Haf']
all_class_df.to_csv('../results/cazy_classes/cazy_class_sizes.csv')
all_class_df

	CAZyClass	Genus	MeanCazyClass	SdCazyClass	MeanClassPerc	SdClassPerc	NumOfGenomes
0	GH	Samsonia	34.00	0.00	41.98	0.00	1
1	GH	Dickeya	42.53	3.55	38.24	1.85	206
2	GH	Acerihabitans	51.00	0.00	48.11	0.00	1
3	GH	Musicola	37.00	0.00	40.13	0.99	4
4	GH	Pectobacterium	50.11	3.91	44.50	1.87	432
5	GH	Brenneria	42.48	6.60	48.14	4.19	33
6	GH	Affinibrenneria	59.00	0.00	54.63	0.00	1
7	GH	Lonsdalea	30.85	2.28	39.96	1.31	39
8	GT	Samsonia	25.00	0.00	30.86	0.00	1
9	GT	Dickeya	37.21	3.12	33.52	2.62	206
10	GT	Acerihabitans	44.00	0.00	41.51	0.00	1
11	GT	Musicola	31.00	3.00	33.55	2.44	4
12	GT	Pectobacterium	31.76	3.86	28.15	2.23	432
13	GT	Brenneria	32.30	2.55	36.90	2.61	33
14	GT	Affinibrenneria	35.00	0.00	32.41	0.00	1
15	GT	Lonsdalea	32.46	2.30	42.07	1.24	39
16	PL	Samsonia	8.00	0.00	9.88	0.00	1
17	PL	Dickeya	16.29	1.72	14.63	1.19	206
18	PL	Acerihabitans	1.00	0.00	0.94	0.00	1
19	PL	Musicola	11.25	0.43	12.19	0.33	4
20	PL	Pectobacterium	14.78	1.36	13.14	0.97	432
21	PL	Brenneria	4.24	1.23	4.81	1.29	33
22	PL	Affinibrenneria	1.00	0.00	0.93	0.00	1
23	PL	Lonsdalea	3.79	0.56	4.92	0.72	39
24	CE	Samsonia	8.00	0.00	9.88	0.00	1
25	CE	Dickeya	7.16	0.80	6.44	0.60	206
26	CE	Acerihabitans	5.00	0.00	4.72	0.00	1
27	CE	Musicola	6.00	0.00	6.51	0.16	4
28	CE	Pectobacterium	7.12	0.83	6.33	0.68	432
29	CE	Brenneria	4.30	1.06	4.93	1.24	33
30	CE	Affinibrenneria	7.00	0.00	6.48	0.00	1
31	CE	Lonsdalea	3.15	0.48	4.09	0.57	39
32	AA	Samsonia	1.00	0.00	1.23	0.00	1
33	AA	Dickeya	1.00	0.00	0.90	0.06	85
34	AA	Acerihabitans	0.00	0.00	0.00	0.00	1
35	AA	Musicola	0.00	0.00	0.00	0.00	4
36	AA	Pectobacterium	1.03	0.16	0.91	0.17	371
37	AA	Brenneria	1.00	0.00	1.27	0.06	8
38	AA	Affinibrenneria	0.00	0.00	0.00	0.00	1
39	AA	Lonsdalea	0.00	0.00	0.00	0.00	39
40	CBM	Samsonia	10.00	0.00	12.35	0.00	1
41	CBM	Dickeya	12.27	1.25	11.03	0.83	206
42	CBM	Acerihabitans	11.00	0.00	10.38	0.00	1
43	CBM	Musicola	11.00	1.00	11.96	1.38	4
44	CBM	Pectobacterium	13.98	1.49	12.41	1.02	432
45	CBM	Brenneria	9.36	0.64	10.74	1.17	33
46	CBM	Affinibrenneria	10.00	0.00	9.26	0.00	1
47	CBM	Lonsdalea	8.74	0.54	11.35	0.63	39
48	GT	Pectobacteriaceae	33.59	1.85	34.87	1.39	660
49	PL	Pectobacteriaceae	7.54	0.66	7.68	0.56	660
50	CBM	Pectobacteriaceae	10.79	0.62	11.18	0.63	660
51	GH	Pectobacteriaceae	43.37	2.04	44.46	1.28	660
52	AA	Pectobacteriaceae	0.50	0.02	0.54	0.04	660
53	CE	Pectobacteriaceae	5.97	0.40	6.17	0.41	660

Very few genomes contained any AA CAZymes. Identify the number of genomes were no AA CAZymes were found, additionally, find the maximum, minimum and mode number of AA CAZymes found across all 660 genomes.

# calc genomes with no AAs
no_aa_genomes = 0
for genus in class_size_dict['AA']:
    for genome in class_size_dict['AA'][genus]:
        no_aa_genomes+=1
print(f"{no_aa_genomes} genomes have no AAs")

aa_counts = [0] * no_aa_genomes
for genus in class_size_dict['AA']:
    for genome in class_size_dict['AA'][genus]:
        aa_counts.append(len(class_size_dict['AA'][genus][genome]['proteins']))
print(f"Max: {max(aa_counts)}\nMin: {min(aa_counts)}\nMode: {statistics.mode(aa_counts)}")

465 genomes have no AAs
Max: 2
Min: 0
Mode: 0

Count the number of genomes were 1 or 2 AA CAZymes were found.

# find genomes with 2 AAs
two_aa_genomes = {}
one_aa_genomes = {}

for genus in class_size_dict['AA']:
    for genome in class_size_dict['AA'][genus]:
        if len(class_size_dict['AA'][genus][genome]['proteins']) == 2:
            try:
                two_aa_genomes[genus].add(genome)
            except KeyError:
                two_aa_genomes[genus] = {genome}
                
        elif len(class_size_dict['AA'][genus][genome]['proteins']) == 1:
            try:
                one_aa_genomes[genus].add(genome)
            except KeyError:
                one_aa_genomes[genus] = {genome}

two_aa_genomes

{'Pectobacterium': {'GCA_000738125.1',
  'GCA_000749915.1',
  'GCA_011378985.1',
  'GCA_011379045.1',
  'GCA_020971565.1',
  'GCA_024343355.1',
  'GCA_024722495.1',
  'GCA_028335745.1',
  'GCA_900195285.2',
  'GCA_900195295.2'}}

for genus in one_aa_genomes:
    print(f"{genus}: {len(one_aa_genomes[genus])}")

Pectobacterium: 361
Dickeya: 85
Brenneria: 8
Samsonia: 1

3. CAZy families

CAZy family frequency dataframe

Calculate the number of CAZymes per CAZy family presented in each genome, where the number of CAZymes is the number of unqiue protein accessions. This value may be greater than the number of CAZymes in the genome because a CAZyme may be annotated with multiple CAZy families.

# make output directory
# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/cazy_families/'), force=True, nodelete=True)

Built output directory: ../results/cazy_families

fam_freq_df = build_fam_freq_df(fgp_df, ['Genus', 'Species'])
fam_freq_df

The dataset contains 117 CAZy families

Counting fam frequencies: 100%|██████████| 717/717 [01:06<00:00, 10.73it/s]

	Genome	Genus	Species	AA10	AA3	CBM0	CBM13	CBM18	CBM3	CBM32	...	PL11	PL17	PL2	PL22	PL26	PL3	PL35	PL38	PL4	PL9
0	GCA_025946765.1	Pectobacterium	cacticida	0	0	0	1	0	1	1	...	1	0	2	1	1	1	0	1	1	2
1	GCA_009874285.1	Dickeya	dianthicola	0	0	0	0	0	0	0	...	0	0	1	1	1	2	0	0	2	3
2	GCA_017897305.1	Dickeya	oryzae	0	0	0	0	0	0	2	...	0	0	1	1	0	2	0	0	1	3
3	GCA_021375765.1	Pectobacterium	punjabense	0	1	0	1	0	1	1	...	0	0	2	1	1	2	0	0	1	2
4	GCA_016107035.1	Pectobacterium	parmentieri	0	0	0	1	0	1	1	...	0	0	2	1	1	1	0	0	1	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
712	GCA_002921235.2	Pectobacterium	versatile	0	1	0	1	0	1	1	...	0	0	2	1	1	2	0	1	0	2
713	GCA_946477055.1	Lonsdalea	britannica	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	1
714	GCA_022747755.1	Dickeya	dianthicola	0	0	0	0	0	0	1	...	0	0	1	1	1	2	0	0	2	3
715	GCA_003595055.1	Pectobacterium	carotovorum	0	1	0	1	0	1	3	...	0	0	2	1	0	2	0	0	1	2
716	GCA_016949795.1	Pectobacterium	brasiliense	0	1	0	1	0	1	1	...	0	0	2	1	1	2	0	0	1	2

717 rows × 120 columns

fam_freq_df.to_csv("../results/cazy_families/cazy_fam_freqs.csv")

Clustermaps

Build clustermap of CAZy family frequencies, with additional row colours marking the genus classification of each genome (i.e. each row).

Prepare the dataframe of CAZy family frequencies:

Index fam_freq_df so that each row name contains the genome, Genus and Species, so that the genomic accession, genus and species is included in the clustermap.

# index the taxonomy data and genome (ggs=genome_genus_species)
fam_freq_df_ggs = copy(fam_freq_df)  # so does not alter fam_freq_df
fam_freq_df_ggs = fam_freq_df_ggs.set_index(['Genome','Genus','Species'])
fam_freq_df_ggs.head(1)

			AA10	AA3	CBM0	CBM13	CBM18	CBM3	CBM32	CBM4	CBM48	CBM5	...	PL11	PL17	PL2	PL22	PL26	PL3	PL35	PL38	PL4	PL9
Genome	Genus	Species
GCA_025946765.1	Pectobacterium	cacticida	0	0	0	1	0	1	1	0	2	1	...	1	0	2	1	1	1	0	1	1	2

1 rows × 117 columns

Colour scheme:

Define a colour scheme to colour code the rows by, in this case by the genus of the species.

To do this, add a column containing the data to be used to colour code each row, e.g. a genus. This extra column is removed by build_row_colours(). The dataframe that is parsed to build_row_colours() must be the dataframe that is used to generate a clustermap, otherwise Seaborn will not be able to map the row oclours correctly and no row colours will be produced.

# define a colour scheme to colour code rows by genus
fam_freq_df_ggs['Genus'] = list(fam_freq_df['Genus'])  # add column to use for colour scheme, is removed
fam_freq_genus_row_colours, fam_g_lut = build_row_colours(fam_freq_df_ggs, 'Genus', 'Set2')

Build a clustermap of CAZy family frequencies:

Use the function build_family_clustermap() from cazomevolve to build clustermaps of the CAZy family frequencies, with different combinations of additional row colours. For example, the row colours could list the genus and/or species classification of each genome.

# make a figure that is full size, and all data is legible
large_fam_clustermap = build_family_clustermap(
    fam_freq_df_ggs,
    row_colours=fam_freq_genus_row_colours,
    fig_size=(40,120),
    file_path="../results/cazy_families/fam_freq_clustermap.svg",
    file_format='svg',
    lut=fam_g_lut,
    legend_title='Genus',
    dendrogram_ratio=(0.2,0.05),
    title_fontsize=28,
    legend_fontsize=24,
    cbar_pos=(0, 0.95, 0.05, 0.05),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

# make a figure the optimal size to fit in a paper
build_family_clustermap(
    fam_freq_df_ggs,
    row_colours=fam_freq_genus_row_colours,
    fig_size=(20,35),
    file_path="../results/cazy_families/paper_fam_freq_clustermap.png",
    file_format='png',
    font_scale=0.5,
    lut=fam_g_lut,
    legend_title='Genus',
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0, 0.95, 0.05, 0.05),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7fedb14e50d0>

Add species classifications

Looking at the species names in the clustermap, there appears to be clustering of the genomes in a manner that correlates not only with their genus classificaiton but also their species classification. Therefore, add an additional row of row-colours, marking the species classification of each genome.

# define a colour scheme to colour code rows by SPECIES
fam_freq_df_ggs['Species'] = list(fam_freq_df['Species'])  # add column to use for colour scheme, is removed
fam_freq_species_row_colours, fam_s_lut = build_row_colours(fam_freq_df_ggs, 'Species', 'rainbow')

# make a figure the optimal size to fit in a paper
build_family_clustermap_multi_legend(
    df=fam_freq_df_ggs,
    row_colours=[fam_freq_genus_row_colours,fam_freq_species_row_colours],
    luts=[fam_g_lut, fam_s_lut],
    legend_titles=['Genus', 'Species'],
    bbox_to_anchors=[(0.2,1.045), (0.63,1.04)],
    legend_cols=[1,5],
    fig_size=(20,40),
    file_path="../results/cazy_families/paper_genus_species_fam_freq_clustermap.png",
    file_format='png',
    font_scale=1,
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0.01, 0.96, 0.1, 0.1),  #left, bottom, width, height
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7feda3fd7760>

Add phenotype classification to clustermap

# define a colour scheme to colour code SOFT vs HARD plant tissue targeting genomes
phenotype_col = []
for ri in range(len(fam_freq_df_ggs)):
    if list(fam_freq_df['Genus'])[ri] in ['Pectobacterium', 'Dickeya', 'Musicola']:
        phenotype_col.append('Soft tissue targeting')
    else:
        phenotype_col.append('Hard tissue targeting')
fam_freq_df_ggs['Phenotype'] = phenotype_col
fam_freq_pheno_row_colours, fam_p_lut = build_row_colours(fam_freq_df_ggs, 'Phenotype', "Set1")

build_family_clustermap_multi_legend(
    df=fam_freq_df_ggs,
    row_colours=[fam_freq_pheno_row_colours, fam_freq_genus_row_colours],
    luts=[fam_p_lut, fam_g_lut],
    legend_titles=['Phenotype', 'Genus'],
    bbox_to_anchors=[(0.225,1.045), (0.63,1.04)],
    legend_cols=[1,5],
    fig_size=(27,41),
    file_path="../results/cazy_families/paper_pheno_genus_fam_freq_clustermap.png",
    file_format='png',
    font_scale=0.7,
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0.01, 0.96, 0.1, 0.1),  #left, bottom, width, height
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7fed94b71d00>

Remove genomes

In the clustermaps the genomes GCA_029023745.1 (Pectobacterium colocasium), GCA_000749925.1 and GCA_000749945.1 (Pectobacterium betavasulorum) contained under estimated representations of their respective CAZomes.

Six Pectobacterium genomes were not included within the main Pectobacterium subtree (dendrogram on the RHS of clustermap):

• GCA_000749925.1 and GCA_000749845.1 (Pectobacterium betavasulorum)
• GCA_000803215.1 (Pectobacterium fontis)
• GCA_025946765.1 (Pectobacterium cacticida)
• GCA_004137815.1 (Pectobacterium zantedeschiae)
• GCA_029023745.1 (Pectobacterium colocasium)

Extracted from the paper:

The genomes appeared to contain fewer total CAZymes (inferred from the lower CAZy family frequencies) than other Pectobacterium genomes, inferring a potential underestimation of their CAZyme features. Genomes GCA_000749925.1, GCA_000749845.1, and GCA_000803215.1 were were listed with the assembly status 'contig' in NCBI (June 2021). Genomic assemblies with the assembly status of 'contig' may contain incomplete genomic sequences. Indeed, the reported CheckM (Parks et al 2015, Genome Res) analysis listed the GCA_000749925.1 and GCA_000749845.1 as missing 5% (100th percentile) of their genomes with 2.25-2.5% contamination, and GCA_000803215.1 as missing 10% (100th percentile). Furthermore, although listed with the assembly status 'complete genome', assembly GCA_025946765.1 was listed as missing 19% (100th percentile) of its genome by CheckM, and the scaffold GCA_004137815.1 was listed as missing 11% (33rd percentile) with 9% contamination. Therefore, the annotated proteomes potentially underestimates the number of features (including CAZymes) in the genomes, and were excluded from the downstream analyses. The genome GCA_029023745.1 was listed with the assembly status 'complete genome', but the NCBI Prokaryotic Genome Annotation Pipeline (PGAGP) output contained a suspiciously high number of frameshifted proteins (greater than 30%), inferring a potentially poor annotation of the genome that may have resulted in an underestimation of its CAZyme features. Therefore, this genome was also excluded from downstream analyses.

genomes_to_remove = [
    'GCA_000749925.1',
    'GCA_000749845.1',
    'GCA_000803215.1',
    'GCA_025946765.1',
    'GCA_004137815.1',
    'GCA_029023745.1',
]
fam_freq_filtered_df = fam_freq_df[~fam_freq_df['Genome'].isin(genomes_to_remove)]
print(f"Original df length: {len(fam_freq_df)}\nLength after removing genome: {len(fam_freq_filtered_df)}")

Original df length: 717
Length after removing genome: 711

Replot the clustermap

Replot the clustermap, exlucding the removed genomes.

fam_freq_filtered_df_ggs = fam_freq_filtered_df.set_index(['Genome', 'Genus', 'Species'])

# make a figure the optimal size to fit in a paper
build_family_clustermap(
    fam_freq_df_ggs,
    row_colours=fam_freq_genus_row_colours,
    fig_size=(20,70),
    file_path="../results/cazy_families/paper_fam_freq_clustermap_FILTERED.svg",
    file_format='svg',
    font_scale=0.5,
    lut=fam_g_lut,
    legend_title='Genus',
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0, 0.95, 0.05, 0.05),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7fed9c38e7f0>

Genus specific CAZy families

Identify CAZy families that are only present in one group, e.g. one Genus, using the function get_group_specific_fams from cazomevolve.

Specifically, get_group_specific_fams returns two dicts:

1. Group specific families: {group: {only unique fams}}
2. All families per group: {group: {all fams}}

all_families = list(fam_freq_df.columns)[3:]
# dict {group: {only unique fams}} and dict {group: {all fams}}
unique_grp_fams, group_fams = get_group_specific_fams(fam_freq_filtered_df, 'Genus', all_families)
unique_grp_fams

Identifying fams in each Genus: 100%|██████████| 711/711 [00:17<00:00, 40.71it/s]
Identifying Genus specific fams: 100%|██████████| 8/8 [00:00<00:00, 16635.81it/s]

{'Dickeya': {'CBM4', 'CE2', 'GH113', 'GH148', 'GH25', 'GH91', 'GT97', 'PL10'},
 'Pectobacterium': {'AA10',
  'CBM13',
  'GH121',
  'GH146',
  'GH18',
  'GT101',
  'GT102',
  'GT11',
  'GT111',
  'GT14',
  'GT24',
  'GT52',
  'PL11'},
 'Brenneria': {'GH106', 'GT21', 'PL17'},
 'Acerihabitans': {'GH127', 'GH15'}}

Identify families that are only found in hard plant tissue targeting genomes, and those families only found in soft plant tissue targeting species.

hard_soft_fams_dict = {'hard': set(), 'soft': set()}

for ri in tqdm(range(len(fam_freq_filtered_df)), desc="Identifying Soft and Hard plant tissue targeting families"):
    genus = fam_freq_filtered_df.iloc[ri]['Genus']
    
    if genus in ['Pectobacterium','Dickeya','Musicola']:
        grp = 'soft'
    else:
        grp = 'hard'
    
    for fam in fam_freq_filtered_df.columns[3:]:
        if fam_freq_filtered_df.iloc[ri][fam] > 1:
            hard_soft_fams_dict[grp].add(fam)

unique_hard_fams = hard_soft_fams_dict['hard'].difference(hard_soft_fams_dict['soft'])
unique_soft_fams = hard_soft_fams_dict['soft'].difference(hard_soft_fams_dict['hard'])

print("Hard plant tissue targeting specific families:")
for fam in unique_hard_fams:
    print(fam)
print("Soft plant tissue targeting specific families:")
for fam in unique_soft_fams:
    print(fam)

Identifying Soft and Hard plant tissue targeting families:   0%|          | 0/711 [00:00<?, ?it/s]

Hard plant tissue targeting specific families:
CE11
GT28
GH77
PL22
GT20
GH37
GH31
CE1
Soft plant tissue targeting specific families:
GT84
GH94
GT1
CBM32
CBM91
PL2
GH102
GH5
CBM0
PL9
GH18
GT41
GH2
GH103
GT56
GT97
PL4
CBM63
AA3
CBM13
GT32
PL26
GT30
GH53
GT19
GH30
GH38

Build into a df that will be similar to one presented in a paper/report.

# convert into df
unique_grp_data = []

unique_grp_fams['Hard tissue'] = unique_hard_fams
unique_grp_fams['Soft tissue'] = unique_soft_fams

for genus in unique_grp_fams:
    new_data = [genus]
    for cazy_class in ['GH', 'GT', 'CE', 'PL', 'AA', 'CBM']:
        added = False
        class_data = []
        for fam in unique_grp_fams[genus]:
            if fam.startswith(cazy_class):
                class_data.append(fam)
                added = True
        if added is False:
            class_data.append("")
        class_data.sort()
        new_data.append(", ".join(class_data))
            
    unique_grp_data.append(new_data)
    
unique_grp_df = pd.DataFrame(unique_grp_data, columns=['Genus', 'GH', 'GT', 'CE', 'PL', 'AA', 'CBM'])
unique_grp_df.to_csv("../results/cazy_families/unique_grp_fams.tsv", sep='\t')
unique_grp_df

	Genus	GH	GT	CE	PL	AA	CBM
0	Dickeya	GH113, GH148, GH25, GH91	GT97	CE2	PL10		CBM4
1	Pectobacterium	GH121, GH146, GH18	GT101, GT102, GT11, GT111, GT14, GT24, GT52		PL11	AA10	CBM13
2	Brenneria	GH106	GT21		PL17
3	Acerihabitans	GH127, GH15
4	Hard tissue	GH31, GH37, GH77	GT20, GT28	CE1, CE11	PL22
5	Soft tissue	GH102, GH103, GH18, GH2, GH30, GH38, GH5, GH53...	GT1, GT19, GT30, GT32, GT41, GT56, GT84, GT97		PL2, PL26, PL4, PL9	AA3	CBM0, CBM13, CBM32, CBM63, CBM91

Pectobacterium and Dickeya genus-specific families

Pectobacterium and Dickeya share a similar plant host range but show notable diversity in the compositions of the CAZyme-complements. drop all genomes not from Pectobacterium and Dickeya from the fam_freq_df dataframe, and repeat the analysis to identify genus specific CAZy families.

all_families = list(fam_freq_filtered_df.columns)[3:]
pd_fam_freq_df_filtered = fam_freq_filtered_df[fam_freq_filtered_df['Genus'].isin(
    ['Pectobacterium', 'Dickeya']
)]
# dict {group: {only unique fams}} and dict {group: {all fams}}
pd_unique_grp_fams, pd_group_fams = get_group_specific_fams(pd_fam_freq_df_filtered, 'Genus', all_families)
pd_unique_grp_fams

Identifying fams in each Genus: 100%|██████████| 632/632 [00:15<00:00, 39.56it/s]
Identifying Genus specific fams: 100%|██████████| 2/2 [00:00<00:00, 14242.12it/s]

{'Dickeya': {'CBM4',
  'CE2',
  'GH113',
  'GH148',
  'GH25',
  'GH26',
  'GH91',
  'GT97',
  'PL0',
  'PL10'},
 'Pectobacterium': {'AA10',
  'CBM13',
  'CBM18',
  'CBM3',
  'CBM67',
  'GH108',
  'GH12',
  'GH121',
  'GH146',
  'GH153',
  'GH154',
  'GH18',
  'GH38',
  'GH65',
  'GH68',
  'GT101',
  'GT102',
  'GT11',
  'GT111',
  'GT14',
  'GT20',
  'GT24',
  'GT32',
  'GT52',
  'GT73',
  'PL11'}}

4. The Core CAZome

Identify CAzy families that are present in every genome in the dataset using identify_core_cazome(), which takes the dataframe of CAZy family frequencies (with only CAZy families included in the columns, i.e no taxonomy columns). These families form the 'core CAZome'.

# make output directory
# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/core_cazome/'), force=True, nodelete=True)

Built output directory: ../results/core_cazome

fam_freq_filtered_df_ggs = fam_freq_filtered_df.set_index(['Genome', 'Genus', 'Species'])

core_cazome = identify_core_cazome(fam_freq_filtered_df_ggs)

core_cazome = list(core_cazome)
core_cazome.sort()

print(f"Total families: {len(all_families)}")
print("The core CAZy families are:")
for fam in core_cazome:
    print('-', fam)

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 6959.18it/s]

Total families: 117
The core CAZy families are:
- CBM5
- CBM50
- GH23
- GH3
- GT2
- GT51
- GT9

The boxplot shows the frequency of each CAZy family across all genomes in the dataframe. We can also break down this data by genus, and build a dataframe of Family, Genus (or tax rank of choice), genome, and frequency.

This dataframe can then be used to build a second dataframe of:

• Family
• Tax rank
• Mean frequency
• SD frequency Which can be presented as is in a report, or imported into RawGraphs to build a matrix plot (aka a proporitonal area plot).

# filter the famil freq df to include only those families in the core CAZome
core_cazome_df = fam_freq_filtered_df_ggs[core_cazome]
plot_fam_boxplot(core_cazome_df, font_scale=0.8, fig_size=(12,6))

The boxplot shows the frequency of each core CAZy family across all Pectobacteriaceae. To break down the frequency by genus, build a dataframe with the mean (and SD) of frequency of each family in the core CAZome per genus. This dataframe can then be used to plot a proportional area plot of the mean frequency of each CAZy family per genus, for exampling using RawGraphs.

core_cazome_df_genus = copy(core_cazome_df)  # to ensure core_cazome_df is not altereted
core_cazome_df_genus = add_tax_column_from_row_index(core_cazome_df_genus, 'Genus', 1)
core_cazome_df_genus.head()

			CBM5	CBM50	GH23	GH3	GT2	GT51	GT9	Genus
Genome	Genus	Species
GCA_009874285.1	Dickeya	dianthicola	2	6	7	4	9	3	4	Dickeya
GCA_017897305.1	Dickeya	oryzae	2	6	6	3	8	3	4	Dickeya
GCA_021375765.1	Pectobacterium	punjabense	1	6	6	3	8	3	3	Pectobacterium
GCA_016107035.1	Pectobacterium	parmentieri	1	6	6	3	10	3	3	Pectobacterium
GCA_024498675.1	Pectobacterium	aroidearum	1	6	8	3	13	4	4	Pectobacterium

core_cazome_fggf_df, core_cazome_mean_freq_df = build_fam_mean_freq_df(
    core_cazome_df_genus,
    'Genus',
    round_by=2,
)

# add rows showing the means across all pectobacteriaceae
all_pecto_core_fam_data = []
for fam in core_cazome_df_genus.columns:
    try:
        mean_freq = np.mean(core_cazome_df_genus[fam]).round(2)
        sd_freq = np.std(core_cazome_df_genus[fam]).round(2)
        all_pecto_core_fam_data.append([fam, 'Pectobacteriaceae', mean_freq, sd_freq])
    except TypeError: # tax column
        continue
    
temp_df = pd.DataFrame(all_pecto_core_fam_data, columns=['Family','Genus','MeanFreq','SdFreq'])
core_cazome_mean_freq_df = pd.concat([core_cazome_mean_freq_df, temp_df])

core_cazome_mean_freq_df.to_csv("../results/core_cazome/core_cazome_freqs.csv")

core_cazome_mean_freq_df

Building [fam, grp, genome, freq] df: 100%|██████████| 711/711 [00:00<00:00, 4189.20it/s]
Building [Fam, grp, mean freq, sd freq] df: 100%|██████████| 8/8 [00:00<00:00, 113.93it/s]

	Family	Genus	MeanFreq	SdFreq
0	CBM5	Samsonia	1.00	0.00
1	CBM50	Samsonia	6.00	0.00
2	GH23	Samsonia	5.00	0.00
3	GH3	Samsonia	2.00	0.00
4	GT2	Samsonia	5.00	0.00
...	...	...	...	...
2	GH23	Pectobacteriaceae	6.49	1.55
3	GH3	Pectobacteriaceae	2.49	0.60
4	GT2	Pectobacteriaceae	8.15	2.10
5	GT51	Pectobacteriaceae	3.08	0.37
6	GT9	Pectobacteriaceae	3.70	0.56

63 rows × 4 columns

Genus specific core CAZomes

As well as look at the core CAZome across all Pectobacteriaceae, identify the core CAZome of each genus. Generate a upsetplot to highlight the differences between the core CAZomes.

Note: Only looking at those genera that are represented by more than one genome, so that a core CAZome can be found. Otherwise, for genera with only one genome representative, all families in that one genome will be listed in the core CAZome.

genera_of_interest = ['Pectobacterium', 'Dickeya', 'Musicola', 'Brenneria', 'Lonsdalea']
all_families = fam_freq_filtered_df_ggs.columns

core_cazomes = {}  # {genus: {fams}}
for genus in genera_of_interest:
    filtered_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'] == genus]
    temp_core_cazome = identify_core_cazome(filtered_df[all_families])
    temp_core_cazome = list(temp_core_cazome)
    temp_core_cazome.sort()
    core_cazomes[genus] = {'fams': temp_core_cazome, 'freqs': {len(filtered_df)}}
    
core_cazomes

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 8937.87it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 12446.01it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 15236.86it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 14754.91it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 14754.91it/s]

{'Pectobacterium': {'fams': ['CBM5',
   'CBM50',
   'GH1',
   'GH103',
   'GH23',
   'GH28',
   'GH3',
   'GH43',
   'GT2',
   'GT51',
   'GT9',
   'PL1',
   'PL2',
   'PL22',
   'PL3',
   'PL9'],
  'freqs': {426}},
 'Dickeya': {'fams': ['CBM48',
   'CBM5',
   'CBM50',
   'CE4',
   'CE8',
   'GH1',
   'GH103',
   'GH105',
   'GH13',
   'GH23',
   'GH28',
   'GH3',
   'GH33',
   'GH73',
   'GH77',
   'GH8',
   'GT1',
   'GT19',
   'GT2',
   'GT28',
   'GT35',
   'GT4',
   'GT5',
   'GT51',
   'GT9',
   'PL1',
   'PL9'],
  'freqs': {206}},
 'Musicola': {'fams': ['CBM48',
   'CBM5',
   'CBM50',
   'CE1',
   'CE11',
   'CE12',
   'CE4',
   'CE8',
   'CE9',
   'GH1',
   'GH102',
   'GH103',
   'GH104',
   'GH105',
   'GH13',
   'GH19',
   'GH2',
   'GH23',
   'GH28',
   'GH3',
   'GH30',
   'GH31',
   'GH32',
   'GH33',
   'GH38',
   'GH5',
   'GH73',
   'GH77',
   'GH8',
   'GT0',
   'GT1',
   'GT19',
   'GT2',
   'GT26',
   'GT28',
   'GT30',
   'GT35',
   'GT4',
   'GT5',
   'GT51',
   'GT56',
   'GT81',
   'GT83',
   'GT9',
   'PL1',
   'PL2',
   'PL22',
   'PL9'],
  'freqs': {4}},
 'Brenneria': {'fams': ['CBM5',
   'CBM50',
   'CE11',
   'CE12',
   'CE9',
   'GH1',
   'GH102',
   'GH103',
   'GH13',
   'GH23',
   'GH28',
   'GH3',
   'GH32',
   'GH4',
   'GH68',
   'GH73',
   'GH94',
   'GT0',
   'GT19',
   'GT2',
   'GT26',
   'GT28',
   'GT30',
   'GT35',
   'GT4',
   'GT5',
   'GT51',
   'GT56',
   'GT8',
   'GT81',
   'GT84',
   'GT9'],
  'freqs': {33}},
 'Lonsdalea': {'fams': ['CBM32',
   'CBM5',
   'CBM50',
   'CE11',
   'CE4',
   'GH19',
   'GH23',
   'GH3',
   'GH32',
   'GH37',
   'GH68',
   'GH77',
   'GH8',
   'GT19',
   'GT2',
   'GT20',
   'GT26',
   'GT28',
   'GT4',
   'GT51',
   'GT56',
   'GT9'],
  'freqs': {39}}}

core_cazome_upsetplot_membership = []
core_cazome_upsetplot_membership = add_to_upsetplot_membership(
    core_cazome_upsetplot_membership,
    core_cazomes,
)
len(core_cazome_upsetplot_membership)

708

core_cazome_upsetplot = build_upsetplot(
    core_cazome_upsetplot_membership,
    sort_by='input',
    file_path='../results/core_cazome/genera_core_cazome.svg',
)

Identify the core CAZome of soft and hard plant tissue targeting genera:

soft_genera = ['Pectobacterium', 'Dickeya', 'Musicola']
hard_genera = ['Brenneria', 'Lonsdalea', 'Samsonia', 'Affinibrenneria', 'Acerihabitans']
grps = [[soft_genera, 'Soft tissue targeting'], [hard_genera, 'Hard tissue targeting']]

all_families = fam_freq_filtered_df_ggs.columns

soft_hard_core_cazomes = {}  # {grp: {fams}}
for grp in tqdm(grps):
    # gather all rows containing the genera of interest
    filtered_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'].isin(grp[0])]
    temp_core_cazome = identify_core_cazome(filtered_df[all_families])
    temp_core_cazome = list(temp_core_cazome)
    temp_core_cazome.sort()
    try:
        soft_hard_core_cazomes[grp[1]]
    except KeyError:
        soft_hard_core_cazomes[grp[1]] = {'fams': set(), 'freqs': [0]}

    soft_hard_core_cazomes[grp[1]]['fams'] = soft_hard_core_cazomes[grp[1]]['fams'].union(
        set(temp_core_cazome)
    )
    soft_hard_core_cazomes[grp[1]]['freqs'][0] += len(filtered_df)
    
soft_hard_core_cazomes

  0%|          | 0/2 [00:00<?, ?it/s]

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 8154.57it/s]

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 14591.70it/s]

{'Soft tissue targeting': {'fams': {'CBM5',
   'CBM50',
   'GH1',
   'GH103',
   'GH23',
   'GH28',
   'GH3',
   'GT2',
   'GT51',
   'GT9',
   'PL1',
   'PL9'},
  'freqs': [636]},
 'Hard tissue targeting': {'fams': {'CBM5',
   'CBM50',
   'CE11',
   'GH23',
   'GH3',
   'GT19',
   'GT2',
   'GT26',
   'GT28',
   'GT4',
   'GT51',
   'GT56',
   'GT9'},
  'freqs': [75]}}

soft_hard_core_cazomes.update(core_cazomes)

core_cazome_upsetplot_membership = []
core_cazome_upsetplot_membership = add_to_upsetplot_membership(
    core_cazome_upsetplot_membership,
    soft_hard_core_cazomes,
)
len(core_cazome_upsetplot_membership)

1419

core_cazome_upsetplot = build_upsetplot(
    core_cazome_upsetplot_membership,
    file_path='../results/core_cazome/genera_soft_hard_core_cazome.svg',
)

5. Families that always occur together

Identify CAZy families that are always present in the genome together - this approach does not tolerate one CAZy family ever appearing without the other family in the same genome.

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/cooccurring_families/'), force=True, nodelete=True)

# reminder of the structure of the df
fam_freq_filtered_df.head(1)

Built output directory: ../results/cooccurring_families

	Genome	Genus	Species	AA10	AA3	CBM0	CBM13	CBM18	CBM3	CBM32	...	PL11	PL17	PL2	PL22	PL26	PL3	PL35	PL38	PL4	PL9
1	GCA_009874285.1	Dickeya	dianthicola	0	0	0	0	0	0	0	...	0	0	1	1	1	2	0	0	2	3

1 rows × 120 columns

Using a correlation matrix:

CAZy families that always appear together can be identified by generating a correlation matrix using the Python package pandas, CAZy families that are always present together will have a correlation matrix of 1.

This can be done using the identify_cooccurring_fams_corrM() function. CAZy families that are always present in the genome (i.e. the core CAZome), or are absent from all genomes will be calulcated to have a correlation score of nan. In order to plot the correlation matrix, the fill_value key word for identify_cooccurring_fams_corrM() can be used to replace all nan values with an interger.

identify_cooccurring_fams_corrM() returns a correlation matrix and ...

all_families = list(fam_freq_filtered_df.columns[3:])

cooccurring_families, fam_corr_M_filled = identify_cooccurring_fams_corrM(
    fam_freq_filtered_df,
    all_families,
    core_cazome=[],
    corrM_path="../results/cooccurring_families/fam_corr_M_filled.csv",
    fill_value=2,
)

Building binary fam freq df: 100%|██████████| 117/117 [00:00<00:00, 1672.98it/s]
Delete absent families: 100%|██████████| 117/117 [00:00<00:00, 7556.84it/s]
Identifying always co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 1832.34it/s]

cooccurring_families

{('CBM4', 'GH148'), ('GH121', 'GH146'), ('GH127', 'GH15'), ('GH94', 'GT84')}

Generate a clustermap of the correlation matrix.

sns.clustermap(
    fam_corr_M_filled,
    cmap=sns.cubehelix_palette(rot=0, dark=2, light=0, reverse=True, as_cmap=True),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7fed75f26430>

An iterative approach to identify co-occurring families:

Iterate through the dataframe of CAZy family frequencies in Pectobacteriaceae (fam_freq_df_filtered) and identify the groups of always co-occurring CAZy families (i.e. those families that are always present together) and count the number of genomes in which the families are present together.

This is done using the cazomevolve function calc_cooccuring_fam_freqs, which returns a dictionary of groups of co-occurring CAZy families. The function takes as input:

1. The dataframe of CAZy family frequencies (it can include taxonomy information in columns)
2. A list of the CAZy families to analyse
3. (Optional) whether to include or exclude the core CAZome from the list of always co-occurring CAZy families.

cooccurring_fams_dict = calc_cooccuring_fam_freqs(
    fam_freq_filtered_df,
    list(all_families),
    exclude_core_cazome=False,
)
cooccurring_fams_dict

Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:01<00:00, 62.30it/s]
Combining pairs of co-occurring families: 100%|██████████| 25/25 [00:00<00:00, 158395.17it/s]

{0: {'fams': {'CBM4', 'GH148'}, 'freqs': {8}},
 1: {'fams': {'CBM5', 'CBM50', 'GH23', 'GH3', 'GT2', 'GT51', 'GT9'},
  'freqs': {711}},
 2: {'fams': {'GH121', 'GH146'}, 'freqs': {1}},
 3: {'fams': {'GH127', 'GH15'}, 'freqs': {1}},
 4: {'fams': {'GH94', 'GT84'}, 'freqs': {309}}}

For each Pectobacteriaceae genus, identify the groups of always co-occurring CAZy families.

Note: Limit the analysis to only those genera represented by more than one genome. Looking at genera where only one genome was analysed, all families in the genome will be listed as always co-occurring.

genera_cooccuring_fams = {}  # {genus: cooccurring_fams_dict}

for genus in tqdm(
    ['Pectobacterium', 'Dickeya', 'Musicola', 'Lonsdalea', 'Brenneria'],
    desc="Identifying genus specific co-occurring fams",
):
    genus_fam_freq_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'] == genus]
    genus_cooccurring_fams_dict = calc_cooccuring_fam_freqs(
        genus_fam_freq_df,
        list(all_families),
        exclude_core_cazome=False,
    )
    genera_cooccuring_fams[genus] = genus_cooccurring_fams_dict
genera_cooccuring_fams

Identifying genus specific co-occurring fams:   0%|          | 0/5 [00:00<?, ?it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
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Combining pairs of co-occurring families: 100%|██████████| 135/135 [00:00<00:00, 257846.56it/s]

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Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 221.31it/s][A

Combining pairs of co-occurring families: 100%|██████████| 361/361 [00:00<00:00, 503539.66it/s]

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Combining pairs of co-occurring families: 100%|██████████| 1130/1130 [00:00<00:00, 778125.68it/s]

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Combining pairs of co-occurring families: 100%|██████████| 500/500 [00:00<00:00, 601592.66it/s]

{'Pectobacterium': {0: {'fams': {'CBM3', 'GH5'}, 'freqs': {425}},
  1: {'fams': {'CBM48', 'CE8', 'CE9', 'GH13'}, 'freqs': {425}},
  2: {'fams': {'CBM5',
    'CBM50',
    'GH1',
    'GH103',
    'GH23',
    'GH28',
    'GH3',
    'GH43',
    'GT2',
    'GT51',
    'GT9',
    'PL1',
    'PL2',
    'PL22',
    'PL3',
    'PL9'},
   'freqs': {426}},
  3: {'fams': {'CE11', 'GH102', 'GH32'}, 'freqs': {425}},
  4: {'fams': {'GH105', 'GT56'}, 'freqs': {425}},
  5: {'fams': {'GH121', 'GH146', 'GH154'}, 'freqs': {1}},
  6: {'fams': {'GH94', 'GT84'}, 'freqs': {152}}},
 'Dickeya': {0: {'fams': {'CBM4', 'GH148'}, 'freqs': {8}},
  1: {'fams': {'CBM48',
    'CBM5',
    'CBM50',
    'CE4',
    'CE8',
    'GH1',
    'GH103',
    'GH105',
    'GH13',
    'GH23',
    'GH28',
    'GH3',
    'GH33',
    'GH73',
    'GH77',
    'GH8',
    'GT1',
    'GT19',
    'GT2',
    'GT28',
    'GT35',
    'GT4',
    'GT5',
    'GT51',
    'GT9',
    'PL1',
    'PL9'},
   'freqs': {206}},
  2: {'fams': {'CE11', 'GT83'}, 'freqs': {204}},
  3: {'fams': {'GH16', 'GT25'}, 'freqs': {1}},
  4: {'fams': {'GH19', 'GH5', 'PL4'}, 'freqs': {203}},
  5: {'fams': {'GH88', 'PL35'}, 'freqs': {3}},
  6: {'fams': {'GH94', 'GT84'}, 'freqs': {89}},
  7: {'fams': {'GT30', 'PL3'}, 'freqs': {205}},
  8: {'fams': {'PL2', 'PL22'}, 'freqs': {205}}},
 'Musicola': {0: {'fams': {'CBM32', 'CBM63'}, 'freqs': {2}},
  1: {'fams': {'CBM48',
    'CBM5',
    'CBM50',
    'CE1',
    'CE11',
    'CE12',
    'CE4',
    'CE8',
    'CE9',
    'GH1',
    'GH102',
    'GH103',
    'GH104',
    'GH105',
    'GH13',
    'GH19',
    'GH2',
    'GH23',
    'GH28',
    'GH3',
    'GH30',
    'GH31',
    'GH32',
    'GH33',
    'GH38',
    'GH5',
    'GH73',
    'GH77',
    'GH8',
    'GT0',
    'GT1',
    'GT19',
    'GT2',
    'GT26',
    'GT28',
    'GT30',
    'GT35',
    'GT4',
    'GT5',
    'GT51',
    'GT56',
    'GT81',
    'GT83',
    'GT9',
    'PL1',
    'PL2',
    'PL22',
    'PL9'},
   'freqs': {4}},
  2: {'fams': {'GH24', 'GH36'}, 'freqs': {2}}},
 'Lonsdalea': {0: {'fams': {'CBM32',
    'CBM5',
    'CBM50',
    'CE11',
    'CE4',
    'GH19',
    'GH23',
    'GH3',
    'GH32',
    'GH37',
    'GH68',
    'GH77',
    'GH8',
    'GT19',
    'GT2',
    'GT20',
    'GT26',
    'GT28',
    'GT4',
    'GT51',
    'GT56',
    'GT9'},
   'freqs': {39}},
  1: {'fams': {'GH1', 'GH28', 'GH4', 'GH73', 'GT0'}, 'freqs': {38}},
  2: {'fams': {'GH13', 'GH39', 'GT30', 'PL1', 'PL3'}, 'freqs': {38}},
  3: {'fams': {'GH26', 'GH51'}, 'freqs': {9}},
  4: {'fams': {'GH31', 'GT81'}, 'freqs': {38}},
  5: {'fams': {'GH78', 'GT1'}, 'freqs': {10}},
  6: {'fams': {'GH94', 'GT84'}, 'freqs': {33}}},
 'Brenneria': {0: {'fams': {'CBM3', 'GH5'}, 'freqs': {25}},
  1: {'fams': {'CBM5',
    'CBM50',
    'CE11',
    'CE12',
    'CE9',
    'GH1',
    'GH102',
    'GH103',
    'GH13',
    'GH23',
    'GH28',
    'GH3',
    'GH32',
    'GH4',
    'GH68',
    'GH73',
    'GH94',
    'GT0',
    'GT19',
    'GT2',
    'GT26',
    'GT28',
    'GT30',
    'GT35',
    'GT4',
    'GT5',
    'GT51',
    'GT56',
    'GT8',
    'GT81',
    'GT84',
    'GT9'},
   'freqs': {33}},
  2: {'fams': {'GH106', 'PL38'}, 'freqs': {1}},
  3: {'fams': {'GH8', 'GT83'}, 'freqs': {15}},
  4: {'fams': {'GT73', 'PL17'}, 'freqs': {1}}}}

Identify families that always co-occurring in soft and hard plant tissue genera.

soft_genera = ['Pectobacterium', 'Dickeya', 'Musicola']
hard_genera = ['Brenneria', 'Lonsdalea', 'Samsonia', 'Affinibrenneria', 'Acerihabitans']
# hard_genera = ['Brenneria', 'Lonsdalea']
grps = [[soft_genera, 'Soft tissue targeting'], [hard_genera, 'Hard tissue targeting']]

for grp in tqdm(grps):
    # gather all rows containing the genera of interest
    grp_fam_freq_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'].isin(grp[0])]
    
    grp_cooccurring_fams_dict = calc_cooccuring_fam_freqs(
        grp_fam_freq_df,
        list(all_families),
        exclude_core_cazome=False,
    )
    genera_cooccuring_fams[grp[1]] = grp_cooccurring_fams_dict

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Build an upset plot of co-occurring CAZy families

Build an upsetplot (using the Python package upsetplot) to visulise the groups of always co-occurring CAZy families, additionally it will plot the number of genomes in which each group of co-occurring CAZy families were present.

First compile the data/membership for the upset plot by:

1. Creating an empty list to store the upset plot data
2. Adding to the empty list the data contained in each dictionary of co-occurring CAZy families by using the add_to_upsetplot_membership() function

upsetplot_membership = []
upsetplot_membership = add_to_upsetplot_membership(upsetplot_membership, cooccurring_fams_dict)

for genus in genera_cooccuring_fams:
    upsetplot_membership = add_to_upsetplot_membership(
        upsetplot_membership,
        genera_cooccuring_fams[genus],
    )

len(upsetplot_membership)

7233

Build the upset plot. This will include the core CAZomes across Pectobacteriaceae, per genus, and per all soft plant tissue targeting genera and all hard plant tissue targeting genera.

pectobact_upsetplot = build_upsetplot(
    upsetplot_membership,
    file_path='../results/cooccurring_families/pecto-cooccurring-families.svg',
)

Break down the incidences per genus:

The upset plot generates a bar chart showing the number of genomes that each group of co-occuring CAZy families appeared in. However, this plots the total number across each of the groups (i.e. Pectobacterium, Dickeya, etc.).

To break down the indidence (i.e. the number of genomes that each group of co-occurring CAZy families were present in) per group, a dataframe listing each group of co-occurring CAZy families, the group (i.e. genus), and the respective frequency must be generated. This dataframe can then be used to generate a proportional area plot (or matrix plot), breaking down the incidence per group (i.e. genus).

The groups of co-occurring CAZy families must be listed in the same order as they are presented in the upset plot.

upset_plot_groups = get_upsetplot_grps(upsetplot_membership)

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Compiling the data of the incidence of each grp of co-occurring CAZy families per group of interest (e.g. per genus), into a single dataframe.

Create an empty list to store all data for the dataframe, then use add_upsetplot_grp_freqs to add data of the incidence per group of co-occurring CAZy families to the list. build_upsetplot_matrix is then used to build the dataframe.

cooccurring_grp_freq_data = []  # empty list to store data for the df

# add pectobacteriaceae data
genera_cooccuring_fams['Pectobacteriaceae'] = cooccurring_fams_dict

# add data for each genus, all soft plant targeting and hard plant tissue targeting
cooccurring_grp_freq_data = add_upsetplot_grp_freqs(
    upset_plot_groups,
    cooccurring_grp_freq_data,
    genera_cooccuring_fams,
    genus,
    grp_sep=True,
    grp_order=[
        'Pectobacteriaceae', 
        'Pectobacterium', 'Dickeya', 'Musicola', 'Soft tissue targeting',
        'Brenneria', 'Lonsdalea', 'Hard tissue targeting',
    ],
    include_none=True,
)

Compiling co-occurring families incidence data: 100%|██████████| 38/38 [00:00<00:00, 20839.90it/s]

Build a single dataframe of co-occurring families, freq and group (e.g. genus).

But also list the information for each group in the same order the groups of CAZy families are listed in the upset plot. This allows a proportional area plot to be generated (for example, by using RawGraphs), which can then be combined with the upset plot (for example, using inkscape).

# build the dataframe
cooccurring_fams_freq_df = build_upsetplot_matrix(
    cooccurring_grp_freq_data,
    'Genus',
    file_path='../results/cooccurring_families/cooccurring_fams_freqs.csv',
)
cooccurring_fams_freq_df

	Families	Genus	Incidence
0	PL2+PL22	Pectobacteriaceae	NaN
1	PL2+PL22	Pectobacterium	NaN
2	PL2+PL22	Dickeya	205.0
3	PL2+PL22	Musicola	NaN
4	PL2+PL22	Soft tissue targeting	635.0
...	...	...	...
299	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Musicola	4.0
300	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Soft tissue targeting	NaN
301	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Brenneria	NaN
302	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Lonsdalea	NaN
303	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Hard tissue targeting	NaN

304 rows × 3 columns

Generate figure for paper

After analysing the data, mannually group of the soft and hard tissue targeting specific groups of CAZy families together and mannually define the order the groups are presented in the final upset plot (by setting the param sort_by to 'input').

grp_order = {
    'soft_grps': [  # grps only found in soft plant tissue targeting genomes
        'GH13+CBM48+CE8', # S
        'PL2+PL22', # # S D
        'GH148+CBM4', # S D
        'PL3+GT30', # D
        'PL35+GH88', # D
        'CE11+GT83', # D
        'GT25+GH16', # D
        'GH5+GH19+PL4', # D
        'GH121+GH146+GH154', # S P   
        'GH121+GH146',
        'GH105+GT56', # P
        'GH13+CBM48+CE8+CE9',  # P
        'CE11+GH32+GH102', # P
        
    ],
    'musicola': [  # grps found only in musicola
        'CBM32+CBM63',
        'GH24+GH36',
    ],
    'both_grps': [  # grps found in soft and hard plant tissue targeting genomes
        'GT84+GH94', # 
        'GH5+CBM3', #
    ],
    'hard_musicola_grps': [  # grps only found in hard plant tissue targeting genomes and Musicola
        'GH13+GT30',
        'GH1+GH73+GT0',
        'GT5+GT35+GT8',
        'GH15+GH127',
        'GT81+GH31', # L
        'GH8+GT83', # B
    ],
    'hard_grps': [ # grps only found in hard plant tissue targeting genomes
        'CBM67+GH65', # H
        'PL17+GT73', # L B
        'PL38+GH106', # L B
        'GT1+GH78', # L
        'GH26+GH51', # L
        'GH1+GH28+GH73+GT0+GH4',
        'GH13+PL1+PL3+GT30+GH39',
    ],
    'all_core_cazomes': [ # then core cazomes at the end
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9',  # pectobacteriaceae
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH28+PL1+GH103+PL9',  # soft plant tissue targeting
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28+PL1+GH103+PL9+CBM48+CE8+GH105+GT4+GT28+GT19+GH73+GT5+GT35+GH8+CE4+GH77+GT1+GH33',   # dickeya
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH28+PL1+GH103+PL9+PL2+PL22+PL3+GH43', # pectobacter 
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28+PL1+GH103+PL9+PL2+PL22+CBM48+CE8+CE11+GH5+GH105+GT56+GH32+GH102+CE9+GT4+GT28+GT19+GH73+GT30+GT5+GT35+GH8+CE4+GH77+GH19+GT83+GT1+GH33+GT26+GT0+GT81+GH31+CE12+GH38+GH30+CE1+GH2+GH104',  # musicola
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+CE11+GT56+GT4+GT28+GT19+GT26',  # hard
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+CE11+GT56+GH32+GT4+GT28+GT19+GH8+CE4+GH77+GH19+GT26+GH68+CBM32+GT20+GH37',   # lonsdalea
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28+GH103+GT84+GH94+CE11+GT56+GH32+GH102+CE9+GT4+GT28+GT19+GH73+GT30+GT5+GT35+GT26+GT0+GT81+GH68+GH4+GT8+CE12', # bren
    ],
}
for grp in grp_order:
    grps = []
    for fams_str in grp_order[grp]:
        fams_list = fams_str.split("+")
        fams_list.sort()
        fams = "+".join(fams_list)
        grps.append(fams)
    grp_order[grp] = grps

paper_cooccurring_fams = {}  # {grp_num: {'fams': {fams}, 'freqs': {int}}}
num_of_grp = 0

for pheno_grp in grp_order:
    for fam_grp in grp_order[pheno_grp]:
        fams = fam_grp.split("+")
        fams.sort()
        
        for genus in ['Pectobacterium', 'Dickeya', 'Musicola', 'Soft tissue targeting', 'Hard tissue targeting', 'Brenneria', 'Lonsdalea']:
            # {grp num: {'fams': {fams}, 'freqs': {int}}}
            
            for grp_num in genera_cooccuring_fams[genus]:
                grp_fams = list(genera_cooccuring_fams[genus][grp_num]['fams'])
                grp_fams.sort()
                
                if grp_fams == fams:
                    
                    this_grp_num = None
                    
                    for co_grp_num in paper_cooccurring_fams:
                        if paper_cooccurring_fams[co_grp_num]['fams'] == genera_cooccuring_fams[genus][grp_num]['fams']:
                            this_grp_num = co_grp_num
                            
                    if this_grp_num is None:
                        this_grp_num = copy(num_of_grp)
                    
                        paper_cooccurring_fams[this_grp_num] = {
                            'fams': genera_cooccuring_fams[genus][grp_num]['fams'],
                            'freqs': genera_cooccuring_fams[genus][grp_num]['freqs']
                        }
                        
                        num_of_grp += 1

upsetplot_membership = []
upsetplot_membership = add_to_upsetplot_membership(upsetplot_membership, paper_cooccurring_fams)
len(upsetplot_membership)

5076

pectobact_upsetplot = build_upsetplot(
    upsetplot_membership,
    file_path='../results/cooccurring_families/paper-pecto-cooccurring-families.svg',
    sort_by='input',
)

Calculate the frequency of each group per genus to then build a matrix plot (or proportional area plot).

paper_cooccurring_freqs = []  # [fams, genus/grp, incidence/freq]
num_of_grp = 0

for grp_name in grp_order:
    for fams in grp_order[grp_name]:
        fams = fams.split("+")
        fams.sort()

        for genus in ['Soft tissue targeting', 'Pectobacterium', 'Dickeya', 'Musicola', 'Hard tissue targeting', 'Brenneria', 'Lonsdalea']:
            # {grp num: {'fams': {fams}, 'freqs': {int}}}

            for grp_num in genera_cooccuring_fams[genus]:
                grp_fams = list(genera_cooccuring_fams[genus][grp_num]['fams'])
                grp_fams.sort()

                if grp_fams == fams:
                    # found fams in genus

                    paper_cooccurring_freqs.append(
                        [
                            genera_cooccuring_fams[genus][grp_num]['fams'],
                            genus,
                            list(genera_cooccuring_fams[genus][grp_num]['freqs'])[0],
                        ]
                    )

# build the dataframe
cooccurring_fams_freq_df = build_upsetplot_matrix(
    paper_cooccurring_freqs,
    'Genus',
    file_path='../results/cooccurring_families/paper-cooccurring_fams_freqs.csv',
)
cooccurring_fams_freq_df

	Families	Genus	Incidence
0	{CE8, GH13, CBM48}	Soft tissue targeting	635
1	{PL22, PL2}	Soft tissue targeting	635
2	{PL22, PL2}	Dickeya	205
3	{CBM4, GH148}	Soft tissue targeting	8
4	{CBM4, GH148}	Dickeya	8
5	{PL3, GT30}	Dickeya	205
6	{GH88, PL35}	Dickeya	3
7	{CE11, GT83}	Dickeya	204
8	{GT25, GH16}	Dickeya	1
9	{GH5, PL4, GH19}	Dickeya	203
10	{GH121, GH146, GH154}	Soft tissue targeting	1
11	{GH121, GH146, GH154}	Pectobacterium	1
12	{GH105, GT56}	Pectobacterium	425
13	{CE9, CE8, GH13, CBM48}	Pectobacterium	425
14	{CE11, GH32, GH102}	Pectobacterium	425
15	{CBM32, CBM63}	Musicola	2
16	{GH36, GH24}	Musicola	2
17	{GT84, GH94}	Soft tissue targeting	241
18	{GT84, GH94}	Pectobacterium	152
19	{GT84, GH94}	Dickeya	89
20	{GT84, GH94}	Hard tissue targeting	68
21	{GT84, GH94}	Lonsdalea	33
22	{GH5, CBM3}	Pectobacterium	425
23	{GH5, CBM3}	Hard tissue targeting	25
24	{GH5, CBM3}	Brenneria	25
25	{GT30, GH13}	Hard tissue targeting	74
26	{GT0, GH1, GH73}	Hard tissue targeting	74
27	{GT8, GT5, GT35}	Hard tissue targeting	36
28	{GH15, GH127}	Hard tissue targeting	1
29	{GT81, GH31}	Lonsdalea	38
30	{GT83, GH8}	Brenneria	15
31	{CBM67, GH65}	Hard tissue targeting	1
32	{GT73, PL17}	Hard tissue targeting	1
33	{GT73, PL17}	Brenneria	1
34	{GH106, PL38}	Hard tissue targeting	1
35	{GH106, PL38}	Brenneria	1
36	{GH78, GT1}	Lonsdalea	10
37	{GH51, GH26}	Lonsdalea	9
38	{GT0, GH4, GH28, GH73, GH1}	Lonsdalea	38
39	{GT30, GH13, GH39, PL1, PL3}	Lonsdalea	38
40	{CBM50, GH28, GH103, GT2, PL9, GH1, GH3, GH23,...	Soft tissue targeting	636
41	{CBM50, CE8, GT1, GH3, GH23, PL1, GH33, CBM5, ...	Dickeya	206
42	{PL3, CBM50, GH28, GH103, GH43, GT2, PL9, GH1,...	Pectobacterium	426
43	{CBM50, CE9, GH104, CE8, GT1, GH32, GH3, GH23,...	Musicola	4
44	{CE11, GT26, CBM50, GT28, GT2, GT56, GH3, GH23...	Hard tissue targeting	75
45	{CBM50, GH32, CBM32, GH23, GH3, GH37, GH19, CB...	Lonsdalea	39
46	{CBM50, GH4, GT84, GH94, CE9, GH32, GH3, GH23,...	Brenneria	33

6. Principal Component Analysis (PCA)

Use principal component analysis to identify individual and groups of CAZy families that are strongly associated with divergence between the Pectobacteriaceae genera CAZomes in terms of CAZy family frequencies.

Use the cazomevolve function perform_pca() to perform a PCA on a dataframe where each row is a genome, and each column the frequency of a unique CAZy family - the columns in the dataframe must only contain numerical data (i.e. no taxonomic data).

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/pca/'), force=True, nodelete=True)

Built output directory: ../results/pca

num_of_components = len(fam_freq_filtered_df_ggs.columns)
pectobact_pca, X_scaled = perform_pca(fam_freq_filtered_df_ggs, num_of_components)
pectobact_pca

PCA(n_components=117)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Explained cumulative variance:

Explore the amount of variance in the dataset that is captured by the dimensional reduction performed by the PCA.

print(
    f"{round(pectobact_pca.explained_variance_ratio_.sum() * 100, 2)}% "
    "of the variance in the data set was catpured by the PCA"
)

cumExpVar = plot_explained_variance(
    pectobact_pca,
    num_of_components,
    file_path="../results/pca/pca_explained_variance.png",
)

100.0% of the variance in the data set was catpured by the PCA
Number of features needed to explain 0.95 fraction of total variance is 59. 

Variance captured per PC:

Explore the variance in the data that is captured by each PC.

plot_scree(pectobact_pca, nComp=10, file_path="../results/pca/pectobact_pca_scree.png")

Explained variance for 1PC: 0.15781670776311302
Explained variance for 2PC: 0.11714531745192662
Explained variance for 3PC: 0.055353539233033315
Explained variance for 4PC: 0.048047116157604305
Explained variance for 5PC: 0.0403160074187065
Explained variance for 6PC: 0.029313990173379986
Explained variance for 7PC: 0.027663834661362113
Explained variance for 8PC: 0.021653599705441392
Explained variance for 9PC: 0.021188563199185637
Explained variance for 10PC: 0.019638464318788094

PC1 (15%) and PC2 (11%) capture a signficantly greater degree of the varaince in the data set than all other PCs.
PC3 (6%) and PC4 (5%) capture comparable degrees of the variance

Scatter and loadings plots

To explore the variance captured by each PC, plot different combinations of PCs onto a scatter plot where each axis represents a different PC and each point on the plot is a genome in the data set, using the plot_pca() function.

plot_pca() takes 6 positional argumets:

1. PCA object from peform_pca()
2. Scaling object (X_scaled) from perform_pca()
3. The dataframe of CAZy family frequencies, if you want to colour code the genomes by a specific grouping (i.e. by Genus), an additional column containing the grouping information needs to be added to the dataframe (e.g. listing the genus per genome)
4. The number of the first PC to be plotted, e.g. 1 for PC1 - int
5. The number of the second PC to be plotted, e.g. 2 for PC2 - int
6. The method to colour code the genomes by (e.g. 'Genus') - needs to match the name of the column containing the data in the dataframe of CAZy family frequencies

Owing to the majoirty of the variance captured by the PCA being captured by PCs 1-4, all possible combinations of these PCs were explored.

PCs 1 - 4

The PCs 1-4 capture more diversity in the data set than the other PCs, therefore, plot all combinations of these PCs against each other, projecting the genomes onto these PCs.

A pairplot is generated using Seaborn, plotting each potential pairs between PCs 1-4. A KDE plot (a special type of density or histogram plot) is generated on the diagonal.

First colour code and style each point (where each point represents a genome) by its genus classification.

fam_freq_filtered_df_ggs['Genus'] = list(fam_freq_filtered_df['Genus'])
fam_freq_filtered_df_ggs['Species'] = list(fam_freq_filtered_df['Species'])

X_pca = pectobact_pca.transform(X_scaled)

fam_freq_df_ggs_pc = copy(fam_freq_filtered_df_ggs)
colnames = []
for i in range(4):
    fam_freq_df_ggs_pc[f'PC{i+1} ({round(pectobact_pca.explained_variance_ratio_[i] * 100, 2)}%)'] = X_pca[:,i]
    colnames.append(f'PC{i+1} ({round(pectobact_pca.explained_variance_ratio_[i] * 100, 2)}%)')

g = sns.pairplot(
    fam_freq_df_ggs_pc,
    vars=colnames,
    hue="Genus",
    diag_kind="kde",
    markers=['o','X', '^', 'P', 'v', 'D', '<', 's'],
    height=3,
);

i = 0
for ax in g.axes.ravel():
    if ax is None:
        continue
    if i not in [0,5,10,15]:
        ax.axhline(0, linestyle='--', color='grey', linewidth=1.25);
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    else:
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    i += 1
    
plt.savefig(
    '../results/pca/pca_pc_screen_genus.svg',
    bbox_inches='tight',
    format='svg'
)

Colour code each point by its species classification.

g = sns.pairplot(
    fam_freq_df_ggs_pc,
    vars=colnames,
    hue="Species",
    diag_kind="kde",
    height=3,
);

i = 0
for ax in g.axes.ravel():
    if ax is None:
        continue
    if i not in [0,5,10,15]:
        ax.axhline(0, linestyle='--', color='grey', linewidth=1.25);
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    else:
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    i += 1
    
sns.move_legend(g, "lower center", bbox_to_anchor=(.5, 1), ncol=6, title='Species', frameon=False);
    
plt.savefig(
    '../results/pca/pca_pc_screen_species.svg',
    bbox_inches='tight',
    format='svg'
)

There are so many species that it is difficult to tell the colours apart. Therefore, pick out some stories of interest, colour code these genomes and leave the rest grey.

# pick out some stories and colour code the species plot accordingly

PC1 separates out the genomes in a manner that correlates with their genus classification: Pectobacterium genomes are locataed in the negative PC1 axis, and Dickeya genomes are located in the positive PC1 axis.

PCs 2-4 do not correlate with the genus classification. In plots (from PC1-PC4) show the genome clustering correlating with species classification.

Individual plots

Having made a pairwise plot. Generate a scatter and loadings plot for each pair of PCs from PC1-PC4. This will make it easier to look at the details in each plot, and will make it easier to tell species apart.

Additional pauses are placed into the code to allow time for the notebook to render to figure before generating the next. This does not impact the figures that are saved to disk, but if the pauses (time.sleep()) are excluded, data from one figure may appear in another.

PC1 vs PC2

pc_pair = (1,2)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Output directory ../results/pca/PC1-vs-PC2 exists, nodelete is True. Adding output to output directory.

PC1 vs PC2 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC1vs PC2 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC1 vs PC2 - Loadings plot

Regenerate the scatter plot of PC1 vs PC2, labelling the Dickeya genomes that are clustered with Musicola, and the Pectobacterium genomes that are on the PC1 +ve axis.

X_pca = pectobact_pca.transform(X_scaled)
plt.figure(figsize=(15,15))
sns.set(font_scale=1.15)
g = sns.scatterplot(
    x=X_pca[:,0],
    y=X_pca[:,1],
    data=fam_freq_filtered_df_ggs,
    hue='Genus',
    style='Genus',
    s=100,
    markers=True,
)

g.axhline(0, linestyle='--', color='grey', linewidth=1.25);
g.axvline(0, linestyle='--', color='grey', linewidth=1.25);

plt.ylabel(f"PC2 {100 * pectobact_pca.explained_variance_ratio_[1]:.2f}%");
plt.xlabel(f"PC1 {100 * pectobact_pca.explained_variance_ratio_[0]:.2f}%");
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0);

genome_lbls = ["-".join(_) for _ in fam_freq_df_ggs.index]
x_vals = X_pca[:,0]
y_vals = X_pca[:,1]

texts = [
    plt.text(
        xval,
        yval,
        lbl,
        ha='center',
        va='center',
        fontsize=12,
    ) for (xval, yval, lbl) in zip(
        x_vals, y_vals, genome_lbls
    ) if ((xval > 2) and (yval < 3.5) and (yval > 0) and (xval < 4)) or ((xval > 0.1) and (xval < 2.5) and (yval < 0))
]
adjustText.adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black'));

plt.savefig('../results/pca/pca_pc1_vs_pc2_musicola_annotated.png', bbox_inches='tight', format='png')

PC1 vs PC3

pc_pair = (1,3)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Output directory ../results/pca/PC1-vs-PC3 exists, nodelete is True. Adding output to output directory.

PC1 vs PC3 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC1 vs PC3 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC1 vs PC3 - Loadings plot

PC1 vs PC4

pc_pair = (1,4)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Output directory ../results/pca/PC1-vs-PC4 exists, nodelete is True. Adding output to output directory.

PC1 vs PC4 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC1 vs PC4 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC1 vs PC4 - Loadings plot

PC2 vs PC3

pc_pair = (2,3)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(8,10),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(8,10),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Output directory ../results/pca/PC2-vs-PC3 exists, nodelete is True. Adding output to output directory.

PC2 vs PC3 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC2 vs PC3 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC2 vs PC3 - Loadings plot

PC2 vs PC4

pc_pair = (2,4)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(10,10),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(10,10),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Output directory ../results/pca/PC2-vs-PC4 exists, nodelete is True. Adding output to output directory.

PC2 vs PC4 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC2 vs PC4 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC2 vs PC4 - Loadings plot

PC3 vs PC4

pc_pair = (3,4)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC3-vs-PC4

PC3 vs PC4 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC3 vs PC4 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC3 vs PC4 - Loadings plot

Generate the scatter plot of PC3 vs PC4 again with only Pectobacterium genomes to identify the species that are diverging from the centre of the plot.

X_pca = pectobact_pca.transform(X_scaled)

plt.figure(figsize=(10,7.5))
sns.set(font_scale=1.15)

temp_d_fam_freq_df_ggs = fam_freq_filtered_df_ggs[fam_freq_filtered_df_ggs['Genus'] == 'Pectobacterium']

g = sns.scatterplot(
    x=X_pca[:,2][fam_freq_filtered_df_ggs['Genus'] == 'Pectobacterium'],
    y=X_pca[:,3][fam_freq_filtered_df_ggs['Genus'] == 'Pectobacterium'],
    data=temp_d_fam_freq_df_ggs,
    hue='Species',
    style='Species',
    s=100,
    markers=True,
)

g.axhline(0, linestyle='--', color='grey', linewidth=1.25);
g.axvline(0, linestyle='--', color='grey', linewidth=1.25);

plt.ylabel(f"PC4 {100 * pectobact_pca.explained_variance_ratio_[3]:.2f}%");
plt.xlabel(f"PC3 {100 * pectobact_pca.explained_variance_ratio_[2]:.2f}%");

plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0);
sns.move_legend(g, "lower center", bbox_to_anchor=(.5, 1), ncol=4, title='Species', frameon=False);

plt.savefig(
    '../results/pca/p_pc3_pc4_pectobacterium_sp.svg',
    bbox_inches='tight',
    format='svg',
)